mc

inst/pydevil/pydevil/interface.py

@@ -10,7 +10,7 @@
 from pydevil.model import model
 from pydevil.guide import guide
 from pydevil.utils import prepare_batch
-from pydevil.utils_input import check_and_prepare_input_run_SVDE, unload_tensor
+from pydevil.utils_input import check_and_prepare_input_run_SVDE, detach_tensor
 from pydevil.utils_hessian import compute_hessians, compute_sandwiches
 
 def run_SVDE(
@@ -115,20 +115,20 @@ def run_SVDE(
     #lk = dist.NegativeBinomial(logits = eta - torch.log(overdispersion) ,
     #    total_count= torch.clamp(overdispersion, 1e-9,1e9)).log_prob(input_matrix).sum(dim = 0)
 
-    ret['input_matrix'] = unload_tensor(ret['input_matrix'])
-    ret['model_matrix'] = unload_tensor(ret['model_matrix'])
-    ret['group_matrix'] = unload_tensor(ret['group_matrix'])
-    ret['sf'] = unload_tensor(ret['sf'])
-    ret['offset_matrix'] = unload_tensor(ret['offset_matrix'])
-    ret['beta_estimate_matrix'] = unload_tensor(ret['beta_estimate_matrix'])
-    ret['dispersion_priors'] = unload_tensor(ret['dispersion_priors'])
-    ret['cluster'] = unload_tensor(ret['cluster'])
-    input_matrix = unload_tensor(input_matrix)
-    model_matrix = unload_tensor(model_matrix)
-    overdispersion = unload_tensor(overdispersion)
-    coeff = unload_tensor(coeff)
-    loc = unload_tensor(loc)
-    UMI = unload_tensor(input_data['sf'])
+    ret['input_matrix'] = detach_tensor(ret['input_matrix'])
+    ret['model_matrix'] = detach_tensor(ret['model_matrix'])
+    ret['group_matrix'] = detach_tensor(ret['group_matrix'])
+    ret['sf'] = detach_tensor(ret['sf'])
+    ret['offset_matrix'] = detach_tensor(ret['offset_matrix'])
+    ret['beta_estimate_matrix'] = detach_tensor(ret['beta_estimate_matrix'])
+    ret['dispersion_priors'] = detach_tensor(ret['dispersion_priors'])
+    ret['cluster'] = detach_tensor(ret['cluster'])
+    input_matrix = detach_tensor(input_matrix)
+    model_matrix = detach_tensor(model_matrix)
+    overdispersion = detach_tensor(overdispersion)
+    coeff = detach_tensor(coeff)
+    loc = detach_tensor(loc)
+    UMI = detach_tensor(input_data['sf'])
 
     # if cuda and torch.cuda.is_available():
     #     input_matrix = input_matrix.cpu().detach().numpy()

inst/pydevil/pydevil/utils_hessian.py

@@ -1,6 +1,6 @@
 import torch
 from tqdm import trange
-from pydevil.utils_input import unload_tensor
+from pydevil.utils_input import detach_tensor
 
 def compute_hessian(obs, model_matrix, coeff, overdispersion, size_factors):
     beta = torch.tensor(coeff)
@@ -34,7 +34,7 @@ def compute_hessians(input_matrix, model_matrix, coeff, overdispersion, size_fac
 
         t.set_description('Variance estimation: {:.2f}  '.format(gene_idx / n_genes))
         t.refresh()
-        solved_hessian = unload_tensor(solved_hessian)
+        solved_hessian = detach_tensor(solved_hessian)
 
         if full_cov:
             loc[gene_idx, :, :] = solved_hessian
@@ -94,7 +94,7 @@ def compute_sandwiches(input_matrix, model_matrix, coeff, overdispersion, size_f
         t.set_description('Clustered variance estimation: {:.2f}  '.format(gene_idx / n_genes))
         t.refresh()
 
-        s = unload_tensor(s)
+        s = detach_tensor(s)
 
         loc[gene_idx, :, :] = s
         del s

inst/pydevil/pydevil/utils_input.py

@@ -20,15 +20,15 @@ def ensure_tensor(obj, cuda):
             print("Error:", e)
             return None
 
-def unload_tensor(obj):
+def detach_tensor(obj):
     """
     Unload the tensor from the GPU.
     """
     if isinstance(obj, torch.Tensor):
         if obj.get_device() == 0:
-            return obj.cpu().detach().numpy()
+            return obj.cpu().detach()
         else:
-            return obj.detach().numpy()
+            return obj.detach()
     return obj
 
 def validate_boolean(parameter, parameter_name):